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investigating-insider-threat-indicators

Enables your agent to investigate insider threat indicators, like data theft attempts by employees.

What it does

This tool helps investigate if an employee is acting suspiciously, like trying to steal company data before leaving. It analyzes activity logs to find unusual patterns.

When to use it

  • ✓When an employee resigns and needs to be monitored.
  • ✓If there are alerts about someone downloading many files.
  • ✓To check if an employee accessed data they shouldn't have.

Example of what to ask your AI

“Investigate employee John Smith for potential data theft.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/investigating-insider-threat-indicators/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/investigating-insider-threat-indicators/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool connects different security systems (like those monitoring downloads and access) and HR data. It looks for activities that are outside the norm for that employee.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill investigating-insider-threat-indicators

Or download just the file:

curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/investigating-insider-threat-indicators/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Investigating Insider Threat Indicators

When to Use

Use this skill when:

  • HR refers a departing employee for monitoring during their notice period
  • DLP alerts indicate bulk data downloads or transfers to personal storage
  • UEBA detects anomalous access patterns deviating significantly from peer baselines
  • Management reports concerns about an employee accessing sensitive data outside their role

Do not use without proper legal authorization — insider threat investigations must be coordinated with HR, Legal, and Privacy teams before monitoring begins.

Prerequisites

  • Legal authorization and HR referral documenting investigation justification
  • SIEM with DLP, endpoint, email, proxy, and authentication log sources
  • Data Loss Prevention (DLP) system (Microsoft Purview, Symantec, Forcepoint) with policy alerts
  • Endpoint monitoring capability (EDR with USB/removable media logging)
  • HR data feed providing employment status, notice dates, and access entitlements
  • Chain of custody procedures for evidence preservation

Workflow

Step 1: Establish Investigation Scope and Legal Authorization

Before any monitoring, ensure proper authorization:

INSIDER THREAT INVESTIGATION AUTHORIZATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Case ID:           IT-2024-0089
Subject:           [Employee Name] — [Department]
Authorized By:     [CISO / General Counsel]
Referral Source:   HR — Employee submitted resignation, 2-week notice
Justification:     Employee has access to trade secrets and customer PII
Scope:             Email, file access, USB, cloud storage, printing
Duration:          2024-03-15 to 2024-03-29 (notice period)
Privacy Review:    Completed — compliant with acceptable use policy

Step 2: Build Activity Timeline from SIEM

Query comprehensive activity for the subject:

index=* (user="jsmith" OR src_user="jsmith" OR sender="jsmith@company.com"
         OR SubjectUserName="jsmith")
earliest="2024-03-01" latest=now
| eval event_category = case(
    sourcetype LIKE "%dlp%", "DLP",
    sourcetype LIKE "%proxy%", "Web Access",
    sourcetype LIKE "%email%", "Email",
    sourcetype LIKE "%WinEventLog%", "Endpoint",
    sourcetype LIKE "%o365%", "Cloud",
    sourcetype LIKE "%vpn%", "VPN",
    sourcetype LIKE "%badge%", "Physical Access",
    1=1, sourcetype
  )
| stats count by event_category, sourcetype, _time
| timechart span=1d count by event_category

Step 3: Detect Data Exfiltration Indicators

Bulk File Downloads (SharePoint/OneDrive):

index=o365 sourcetype="o365:management:activity" Operation IN ("FileDownloaded", "FileSynced")
UserId="jsmith@company.com" earliest=-30d
| stats count AS downloads, sum(eval(if(isnotnull(FileSize), FileSize, 0))) AS total_bytes,
        dc(SourceFileName) AS unique_files
  by UserId, SiteUrl, _time
| bin _time span=1d
| eval total_gb = round(total_bytes / 1073741824, 2)
| where downloads > 50 OR total_gb > 1
| sort - total_gb

USB/Removable Media Usage:

index=sysmon EventCode=1 Computer="WORKSTATION-JSMITH"
(CommandLine="*removable*" OR CommandLine="*usb*"
 OR Image="*\\xcopy*" OR Image="*\\robocopy*")
| table _time, Computer, User, Image, CommandLine
| append [
    search index=endpoint sourcetype="endpoint:device_connect"
    user="jsmith" device_type="removable"
    | table _time, user, device_name, device_serial, action
  ]
| sort _time

Email-Based Exfiltration:

index=email sourcetype="o365:messageTrace"
SenderAddress="jsmith@company.com"
| eval is_external = if(match(RecipientAddress, "@company\.com$"), 0, 1)
| eval has_attachment = if(isnotnull(AttachmentName), 1, 0)
| stats count AS total_emails,
        sum(is_external) AS external_emails,
        sum(has_attachment) AS with_attachments,
        sum(eval(if(is_external=1 AND has_attachment=1, 1, 0))) AS external_with_attach,
        sum(Size) AS total_size_bytes
  by SenderAddress
| eval external_attach_pct = round(external_with_attach / total_emails * 100, 1)
| eval total_size_mb = round(total_size_bytes / 1048576, 1)

Cloud Storage Upload Detection:

index=proxy user="jsmith"
(dest IN ("*dropbox.com", "*drive.google.com", "*onedrive.live.com",
          "*box.com", "*wetransfer.com", "*mega.nz")
 OR category="cloud-storage")
http_method=POST
| stats count AS uploads, sum(bytes_out) AS total_uploaded
  by user, dest, category
| eval uploaded_mb = round(total_uploaded / 1048576, 1)
| sort - uploaded_mb

…

FAQ

Do I need permission to use this?

Yes, it's crucial to have legal and HR authorization before starting any investigation. This ensures everything is done according to laws and company policies.

What data is analyzed?

The tool analyzes logs from security systems like SIEM, DLP (data loss prevention), endpoint access (computers), emails, and HR data about the employee.

What are 'insider threat indicators'?

These are signs that an employee might be acting maliciously, such as trying to copy confidential data, accessing systems without permission, or violating company policies.

Details

Original description by the author

Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation. Use when SOC teams receive insider threat referrals from HR, detect anomalous data movement by employees, or need to build investigation timelines for potential insider threats. '

repo: mukul975/Anthropic-Cybersecurity-Skills

Popularity

GitHub stars
35k
Ranking position
#5989
License
Apache-2.0
Updated
8/31/2026

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